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The potential of time-multiplexed steering in phased array microwave hyperthermia for head and neck cancer treatment

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PHYSICS IN MEDICINE AND BIOLOGY 2018年第13期63卷 135023-135023页

作者： Cappiello, G. Drizdal, T. Mc Ginley, B. O'Halloran, M. Glavin, M. van Rhoon, G. C. Jones, E. Paulides, M. M. Natl Univ Ireland Coll Engn & Informat Discipline Elect & Elect Engn Galway Ireland Erasmus MC Canc Inst Dept Radiat Oncol Hyperthermia Unit Rotterdam Netherlands Galway Mayo Inst Technol Dept Comp Sci & Appl Phys Galway Ireland Natl Univ Ireland Lambe Inst Translat Res Translat Med Device Lab Galway Ireland

Clinical studies have shown that hyperthermia sensitizes tumor cells for conventional therapies. During phased-array microwave hyperthermia, an array of antennas is used to focus the electromagnetic waves at the target region. Selective heating, while preserving the healthy tissue, is a demanding challenge and currently patient specific pre-treatment planning is used to optimize the amplitudes and phases of the waves. In addition, when needed, this single optimal heat distribution is adapted using the simulations based on the feedback from thermo-sensors and the patient. In this paper, we hypothesize that sequential, i.e. 'time-multiplexed', application of multiple Pareto optimal heating patterns provides a better time-averaged treatment quality. To test the benefit of such a time-multiplexed approach, a multi-objective genetic algorithm was introduced to balance two objectives that both focus the specific absorption rate (SAR) delivered to the target region but differ in the suppressing of pre-defined hotspots. This step leads to two Pareto optimal distributions. These 'diverse' antenna settings are then applied sequentially and thermal simulations are used to evaluate the effectiveness of the time-multiplexed steering. The proposed technique is tested using treatment planning data of a representative dataset of five head and neck patients for the HYPERcollar3D. Steering dynamics are analysed and the time-multiplexed steering is compared to the current static solution used in the clinic, i.e. hotspot-target SAR quotient optimization using particle swarm optimization. Our results demonstrate that realistic steering periods of 10s suffice to stabilize temperatures within 0.04 degrees C and the ability to enhance target heating while reducing hotspots, i.e. 0.3 degrees C-1.2 degrees C improvement in T-50 while reducing hotspot temperatures by 0.6 degrees C-1.5 degrees C.

关键词： hyperthermia hyperthermia treatment planning multi-objective genetic algorithm

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multi-objective optimization on structural parameters of torsional flow heat exchanger

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APPLIED THERMAL ENGINEERING 2019年 161卷 113831-000页

作者： Gu, Xin Wang, Tongtong Chen, Weijie Luo, Yuankun Tao, Zhilin Zhengzhou Univ Key Lab Proc Heat Transfer & Energy Saving Henan Zhengzhou 450002 Henan Peoples R China CNTC Zhengzhou Tobacco Res Inst Zhengzhou 450001 Henan Peoples R China

Torsional flow heat exchanger is a new type of shell-and-tube heat exchanger with high heat transfer efficiency and low flow resistance. In this paper, the holistic model of the torsional flow heat exchanger is established, and the pressure drop, heat transfer coefficient and thermal performance factor in the shell-side are numerically studied. In order to achieve a better heat transfer performance in a torsional flow heat exchanger, the multi objective structure optimization on the baffle was carried out by using the method of response surface optimization. The results show that the shell-side pressure drop is significantly affected by the angle and width of the baffle;however, for the heat transfer performance, such as the heat transfer coefficient, is mainly attributed to the inclination angle. Three parameters were fitted with the multi-objective genetic algorithm in Ansys, with which the heat transfer performance of the torsional heat exchanger is improved by the value of 15.17%. This study provides a novel method to estimate and optimize the structure of the shell-and-tube heat exchanger.

关键词： Shell-and-tube heat exchanger Torsional flow Structural parameter multi-objective genetic algorithm Response surface method Numerical simulation

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Sensitivity and stress analysis of serrated fin structure in plate-fin heat exchanger on cryogenic condition

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2019年 145卷 106013-000页

作者： Li, Ke Wen, Jian Yang, Huizhu Wang, Simin Li, Yanzhong Xi An Jiao Tong Univ Dept Refrigerat & Cryogen Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Dept Proc Equipment & Control Engn Xian 710049 Shaanxi Peoples R China Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Ctr Microflows & Nanoflows Shenzhen 518055 Peoples R China

The effects and interaction effects of fin height, fin space, fin thickness and fin interrupted length on flow resistance, heat transfer and stress of plate fin heat exchanger (PFHE) on cryogenic condition are analyzed quantitatively, based on computational fluid dynamics (CFD), fluid structure interaction (FSI), response surface methodology (RSM) and sobol sensitivity analysis. The stress distribution in serrated fin structure shows that the large stress is mainly distributed in the regions of keen-edged geometric structure such as weld joint region and the region connecting two rows of fin. The sensitivity analysis results reveal that j factor is the most sensitive to fin interrupted length, and f factor and maximum stress are most sensitive to fin thickness. The sobol sensitivity analysis results show that, for j factor and maximum stress, it is sufficient to investigate the first-order sensitivity indices and high-order sensitivity is weak. For f factor, it is necessary to consider the second-order sensitivity, especially for fin space and fin thickness. Based on above analyses, by combining with multi-objective genetic algorithm (MOGA), the serrated fin structure is optimized from perspective of two objectives (maximizing JF factor and minimizing maximum stress) and three objectives (maximizing j factor, minimizing f factor and minimizing maximum stress). In general, the optimization results based on two objectives are more intuitive, and they offer a smaller solution space. An original point is optimized, the results reveal that, compared with original point, JF factor of optimized point 1 increases by 17.8% without changing maximum stress, while maximum stress of optimized point 2 decreases by 50.0% without changing JF factor.

关键词： Plate-fin heat exchanger Serrated fin Cryogenic condition Stress analysis Response surface multi-objective genetic algorithm Optimization

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Thermal-Hydraulic Performance and Optimization of Tube Ellipticity in a Plate Fin-And-Tube Heat Exchanger

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JOURNAL OF ELECTRONIC PACKAGING 2019年第3期141卷 031008页

作者： Zhu, Hua Yang, Zhuo Khan, Tariq Amin Li, Wei Sun, Zhijian Du, Jincai Zhang, Zhengjiang Zhou, Jianxin Zhejiang Univ Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Coinnovat Ctr Adv Aeroengine Dept Energy Engn Hangzhou 310027 Zhejiang Peoples R China NFC Inst Engn & Technol Dept Mech Engn PO Fertilizer ProjectKhanewal Rd Multan 60000 Pakistan Hangzhou Yuhu Technol Co Ltd Hangzhou 310027 Zhejiang Peoples R China

The flow field inside the heat exchangers is associated with maximum heat transfer and minimum pressure drop. Designing a heat exchanger and employing various techniques to enhance its overall performance has been widely investigated and is still an active research. The application of elliptic tube is an effective alternative to circular tube which can reduce the pressure drop significantly. In this study, numerical simulation and optimization of variable tube ellipticity is studied. The three-dimensional numerical analysis and a multi-objective genetic algorithm (MOGA) with surrogate modeling are performed. Tubes in staggered arrangement in fin-and-tube heat exchanger are investigated for combination of various elliptic ratios and Reynolds numbers. Results show that increasing elliptic ratio increases the friction factor due to increased flow blocking area, however, the effect on the Colburn factor is not significant. Moreover, tube with lower elliptic ratio followed by higher elliptic ratio tube has better thermal-hydraulic performance. To achieve the best overall performance, the Pareto optimal strategy is adopted for which the computational fluid dynamics (CFD) results, artificial neural network (ANN), and MOGA are combined. The tubes elliptic ratio and Reynolds number are the design variables. The objective functions include Colburn factor (j) and friction factor (f). The CFD results are input into ANN model. Once the ANN is computed, it is then used to estimate the model responses as a function of inputs. The final trained ANN is used to drive the MOGA to obtain the Pareto optimal solution. The optimal values of these parameters are finally presented.

关键词： fin-and-tube heat exchanger thermal-hydraulic performance CFD neural network multi-objective genetic algorithm

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Fuzzy multi-SVR Learning Model for Reliability-Based Design Optimization of Turbine Blades

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MATERIALS 2019年第15期12卷 2341-2341页

作者： Zhang, Chun-Yi Wang, Ze Fei, Cheng-Wei Yuan, Zhe-Shan Wei, Jing-Shan Tang, Wen-Zhong Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin 150080 Heilongjiang Peoples R China Fudan Univ Dept Aeronaut & Astronaut Shanghai 200433 Peoples R China Beihang Unviers Sch Comp Sci & Engn Beijing 100191 Peoples R China

The effectiveness of a model is the key factor of influencing the reliability-based design optimization (RBDO) of multi-failure turbine blades in the power system. A machine learning-based RBDO approach, called fuzzy multi-SVR learning method, was proposed by absorbing the strengths of fuzzy theory, support vector machine of regression (SVR), and multi-response surface method. The model of fuzzy multi-SVR learning method was established by adopting artificial bee colony algorithm to optimize the parameters of SVR models and considering the fuzziness of constraints based on fuzzy theory, in respect of the basic thought of multi-response surface method. The RBDO model and procedure with fuzzy multi-SVR learning method were then resolved and designed by multi-objective genetic algorithm. Lastly, the fuzzy RBDO of a turbine blade with multi-failure modes was performed regarding the design parameters of rotor speed, temperature, and aerodynamic pressure, and the design objectives of blade stress, strain, and deformation, and the fuzzy constraints of reliability degree and boundary conditions, as well. It is revealed (1) the stress and deformation of turbine blade are reduced by 92.38 MPa and 0.09838 mm, respectively. (2) The comprehensive reliability degree of the blade was improved by 3.45% from 95.4% to 98.85%. (3) It is verified that the fuzzy multi-SVR learning method is workable for the fuzzy RBDO of complex structures just like a multi-failure blade with high modeling precision, as well as high optimization, efficiency, and accuracy. The efforts of this study open a new research way, i.e., machine learning-based RBDO, for the RBDO of multi-failure structures, which expands the application of machine learning methods, and enriches the mechanical reliability design method and theory as well.

关键词： reliability-based design optimization turbine blades multi-objective genetic algorithm fuzzy support vector machine of regression uncertainty

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Optimal Allocation Model of Water Resources in Tidal Flat Development of Coastal Areas

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JOURNAL OF COASTAL RESEARCH 2018年第Spe84期34卷 89-93页

作者： Huang, Xianfeng Chen, Yingqin Fang, Guohua Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Jiangsu Peoples R China Hohai Univ Wentian Coll Dept Civil Engn Maanshan Peoples R China

As a result of economic and population development in coastal areas, the conflict of water utilization is becoming increasingly serious. To ensure rational utilization, the optimal allocation model of water resources was constructed for the development of coastal tidal flats based on the analysis of the coastal area development model. The goal of optimal allocation was to achieve optimal economic, social, and ecological benefits under the conditions of coordinated use of water and soil resources. Due to the complexity of the water resources system, the model used various land areas, in addition to economic, social, and ecological data, to establish target measures, and a multi-objective genetic algorithm was used to solve the problem. Following each iteration, individuals' fitness was ranked and constantly updated according to population evolution, since individuals learn from the fittest and genes are improved accordingly. The coastal area of Dongtai in Jiangsu province, China, was considered a case study, in which the scheme of the city's water resources was optimized. The optimal allocation results show that by 2020, the agricultural and construction land will have increased, and the unused land will decrease, with the construction land area being 36185 hm2;thus, conforming to the future trend of Dongtai land utilization. The results verify the rationality and feasibility of the model and the algorithm, which provides ideas and experience for the optimal allocation of water resources during beach development in coastal areas.

关键词： Beach development the optimization of water resources multi-objective genetic algorithm coastal areas

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An optimal image watermarking approach based on a multi-objective genetic algorithm

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INFORMATION SCIENCES 2011年第24期181卷 5501-5514页

作者： Wang, Jun Peng, Hong Shi, Peng Xihua Univ Sch Elect & Informat Engn Chengdu 610039 Sichuan Peoples R China Xihua Univ Sch Math & Comp Engn Chengdu 610039 Sichuan Peoples R China Univ Glamorgan Dept Comp & Math Sci Pontypridd CF37 1DL M Glam Wales Victoria Univ Sch Sci & Engn Melbourne Vic 8001 Australia

In accordance with the multi-objective nature of image watermarking, an optimal image watermarking approach using a multi-objective genetic algorithm is presented in this paper. Both watermarking parameters and embedding positions are often important factors affecting the performance of watermarking systems. The proposed multi-objective watermarking method can automatically optimize system parameters, and a variable-length mechanism is specially designed to search the most suitable positions for embedding watermarks. The method can also remove the difficult issue of determining optimal watermarking parameters from previous watermarking algorithms. The proposed multi-objective watermarking method directly deals with the problem of optimizing watermarking under non-dominated meaning, thus it can effectively avoid the difficulty of determining the optimally weighted factor in existing single-objective watermarking schemes. In addition, a Pareto-optimal set generated by multi-objective optimization can provide flexibility in selecting the most suitable watermarking parameters according to practical requirements. (C) 2011 Elsevier Inc. All rights reserved.

关键词： multi-objective genetic algorithm Variable-length mechanism Image watermarking Imperceptibility Robustness

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Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9597-9602页

作者： Arkat, Jamal Hosseini, Leila Farahani, Mehdi Hosseinabadi Univ Kurdistan Dept Ind Engn Sanandaj Iran

Cell formation problem is the main issue in designing cellular manufacturing systems. The most important objective in the cell formation problem is to minimize the number of exceptional elements which helps to reduce the number of intercellular movements. Another important but rarely used objective function is to minimize the number of voids inside of the machine cells. This objective function is considered in order to increase the utilization of the machines. We present a bi-objective mathematical model to simultaneously minimize the number of exceptional elements and the number of voids in the part machine incidence matrix. An epsilon-constraint method is then applied to solve the model and to generate the efficient solutions. Because of the NP-hardness of the model, the optimal algorithms can not be used in large-scale problems and therefore, we have also developed a bi-objective genetic algorithm. Some numerical examples are considered to illustrate the performance of the model and the effectiveness of the solution algorithms. The results demonstrate that in comparison with the epsilon-constraint method, the proposed genetic algorithm can obtain efficient solution in a reasonable run time. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cell formation problem Exceptional elements Voids epsilon-constraint method multi-objective genetic algorithm

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Shape Optimization in Product Design Using Interactive genetic algorithm Integrated with multi-objective Optimization 10th

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10th multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI)

作者： Kielarova, Somlak Wannarumon Sansri, Sunisa Naresuan Univ Fac Engn ID3 Ind Design Decis & Dev Res Unit Phitsanulok Thailand Naresuan Univ Fac Engn Dept Ind Engn Phitsanulok Thailand

ISBN: (纸本)9783319493978;9783319493961

This paper proposes an interactive genetic algorithm (IGA) integrated with multi-objective genetic algorithm (MOGA) in development of a generative design system. IGA is used in initializing and handling single dimensionally qualitative objectives. MOGA is used in optimizing two quantitative objectives. Qualitative factors are considered as design objectives to be optimized together with quantitative criteria. The multi-objective optimization is regarded to concurrent handling of two quantitative criteria. Shape of product is modeled by parametric modeling with Rhinoceros and Grasshopper. IGA is processed using Galapagos in Grasshopper. Shape optimization of the product is processed by using MOGA in MATLAB and linked to Grasshopper. Pareto-optimal front is generated to show the optimal solutions, which is able to support designers in decision making. The perfume bottle design is used as an illustration of the proposed framework, but the framework is applicable to other design problems.

关键词： Generative design Interactive genetic algorithm multi-objective genetic algorithm Qualitative multi-objective optimization Pareto-optimal front Bottle design

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Fractional order [PI] Controller and Smith-like Predictor Design for A Class of High Order Systems

Fractional order [PI] Controller and Smith-like Predictor De...

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第30届中国过程控制会议（CPCC 2019）

作者： Zhenlong Wu Jie Yuan Yuquan Chen Donghai Li YangQuan Chen State Key Lab of Power Systems Department of Energy and Power Engineering Tsinghua University School of Automation Southeast University Department of Automation University of Science and Technology of China Mechatronics Embedded Systems and Automation(MESA)Lab School of Engineering University of California

To handle the control difficulties caused by high-order dynamics, a control structure based on fractional order [proportional integral]（PI） controller and fractional order Smith-like predictor for a class of high order systems in the type of K/（Ts+1）~n is proposed in this *** tracking performance and disturbance rejection ability are analyzed based on the terminal value *** shows that the system output can converge to the set point without static error when set point changes and input disturbance *** on the multi-objective genetic algorithm（MO-GA）, simulations under different χ are carried out to analyze the influence on control performance and control *** results show that the control performance can be improved and the energy of the control signal can be reduced simultaneously when the order χ is chosen no more than *** can verify that the fractional order Smith-like predictor with χ<1 has an advantage over the integral order Smith-like predictor with χ=1.

关键词： Fractional order [PI] Controller fractional order Smith-like predictor high order systems multi-objective genetic algorithm

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